02 December 2016

Structure

  • Introduction
  • Data
  • Descriptive Statistics
  • Empirical Strategy
  • Results

Trend Sport of the 21st Century?

Background

Binge Drinking – A Problem?

German crime statistics: violent crime increasingly committed under the influence of alcohol

Adolescents especially affected

Research Questions

What explains regional differences? Correlation with economic situation?

Night sales ban on alcohol: any effect on binge drinking in Baden-Wuerttemberg?

Data

Variables Reasoning
F10.0 cases per 1000 Number of people hospitalized with acute alcohol intoxication
K70 cases per 1000 Number of people hospitalized diagnosed with alcohol-related liver disease
GDP per capita Value and change between years as indicator of economic development
(Youth) unemployment rate Indicator of economic situation for age groups
Population density Indicator for urbanization
Treatment dummies (local & temporal) Capture the policy measure effect

Data Visuals - F10.0 per Age Group

Data Visuals - K70 per Age Group

Data Visuals - F10.0 Cases in Germany

Data Visuals - F10.0 Increase Over Time

Data Visuals - F10.0 Increase - 15-19 y/o

Empiricial Strategy

  • Dependent variable: Alcohol-related hospitalization (per 1000)

Question 1: Differences between states

Method: Multiple regression

  • Variation: Levels and differences
  • Robustness check: Different years and long vs. short-term alcohol diagnosis

Question 2: Effect of night-sale-ban

Method: Difference-in-Difference

  • Variation: Control variables and number of periods
  • Robustness check: different control group compositions

Results

Conclusion

Appendix 1

Regression results for Model 1 for 2000, 2007 and 2014
Dependent variable:
F10.0 Diagnoses per 1000 capita
2000 2007 2014
GDP per capita -0.03*** -0.02 -0.02*
(0.01) (0.02) (0.01)
Unemployment rate -0.05*** -0.05 -0.08
(0.01) (0.03) (0.04)
Beer tax 0.01** 0.01 0.03*
(0.01) (0.01) (0.01)
Population density 0.0001** -0.0001 -0.0001
(0.0000) (0.0001) (0.0001)
(Intercept) 1.76*** 2.32*** 2.57***
(0.26) (0.65) (0.54)
Observations 16 16 16
Adjusted R2 0.57 0.42 0.61
Residual Std. Error (df = 11) 0.12 0.28 0.25
Note: p<0.1; p<0.05; p<0.01

Appendix 2

Regression results for Model 2 with first differenced data
Dependent variable:
Change in F10.0 Change in F10.2 Change in K70
GDP Change 0.02*** -0.01 0.002
(0.01) (0.01) (0.002)
Unemployment Change -0.01 -0.04** 0.002
(0.01) (0.01) (0.003)
Beer Tax Change -0.0001 0.02* -0.001
(0.01) (0.01) (0.002)
Observations 224 224 224
Adjusted R2 0.08 0.03 -0.01
Residual Std. Error (df = 221) 0.11 0.18 0.04
Note: p<0.1; p<0.05; p<0.01

Appendix 3

Model 3: simple differences-in-differences
Dependent variable:
F10.0 cases per 1000 PP
All States All but City States
DE-BW dummy 0.12 0.04
(0.12) (0.11)
Post-treatment dummy 0.43*** 0.48***
(0.05) (0.05)
Interaction -0.11 -0.16
(0.21) (0.18)
(Intercept) 0.96*** 1.04***
(0.03) (0.03)
Observations 255 210
Adjusted R2 0.22 0.31
Residual Std. Error 0.37 (df = 251) 0.32 (df = 206)
Note: p<0.1; p<0.05; p<0.01

Appendix 4

Model 4: simple differences-in-differences with controls
Dependent variable:
F10.0 cases per 1000 PP
All States
DE-BW dummy -0.16
(0.11)
Post-sales ban dummy 0.32***
(0.05)
GDP per capita -0.02***
(0.003)
Youth unemployment rate -0.06***
(0.01)
Interaction dummy 0.04
(0.18)
(Intercept) 2.30***
(0.13)
Observations 240
Adjusted R2 0.45
Residual Std. Error 0.32 (df = 234)
Note: p<0.1; p<0.05; p<0.01

Appendix 5

Model 5: multi-period panel differences-in-differences
Dependent variable:
F10.0 cases per 1000 PP
All States
DE-BW ban dummy -0.06
(0.09)
Youth unemployment rate -0.02**
(0.01)
GDP per capita -0.01
(0.02)
Observations 240
Adjusted R2 0.02
Note: p<0.1; p<0.05; p<0.01

Appendix 6